Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model
As one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2018-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25892538/view |
| _version_ | 1818539082637115392 |
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| author | Jing Liu Yacheng An Runliang Dou Haipeng Ji |
| author_facet | Jing Liu Yacheng An Runliang Dou Haipeng Ji |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | As one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault diagnosis is difficult to characterize the complex mapping between the massive fault data, the exponentially grown and newly generated data is learned repeatedly, and these approaches cannot incrementally correct the model to adapt the situation that the states and properties of equipment are changed over time, resulting in the increase of time costs and the decrease of diagnosis accuracy of model. In this paper, a dynamic deep learning algorithm based on incremental compensation is proposed. Firstly, the feature modes of the newly generated data are extracted by using deep learning algorithm; it is then compared with the fault modes extracted from the historical data. Next, a similarity computing model is presented to dynamically adjust the weights of incrementally merged modes. Finally, the SVM algorithm is employed to classify the weighted modes by supervised way, and the BP algorithm utilized to fine tune the model, in order to complete the dynamic and compensatory adjustment of deep learning with original modes and incremental modes. The experimental results of bearing running data demonstrate that the proposed approach could significantly improve the accuracy of diagnosis and save the time cost, contributing to meet the varied needs of the real-time equipment fault diagnosis. |
| first_indexed | 2024-12-11T21:37:21Z |
| format | Article |
| id | doaj.art-2accaf8648be4b23aa83aca28ce81d0f |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T21:37:21Z |
| publishDate | 2018-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2accaf8648be4b23aa83aca28ce81d0f2022-12-22T00:49:57ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832018-01-0111110.2991/ijcis.11.1.64Dynamic deep learning algorithm based on incremental compensation for fault diagnosis modelJing LiuYacheng AnRunliang DouHaipeng JiAs one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault diagnosis is difficult to characterize the complex mapping between the massive fault data, the exponentially grown and newly generated data is learned repeatedly, and these approaches cannot incrementally correct the model to adapt the situation that the states and properties of equipment are changed over time, resulting in the increase of time costs and the decrease of diagnosis accuracy of model. In this paper, a dynamic deep learning algorithm based on incremental compensation is proposed. Firstly, the feature modes of the newly generated data are extracted by using deep learning algorithm; it is then compared with the fault modes extracted from the historical data. Next, a similarity computing model is presented to dynamically adjust the weights of incrementally merged modes. Finally, the SVM algorithm is employed to classify the weighted modes by supervised way, and the BP algorithm utilized to fine tune the model, in order to complete the dynamic and compensatory adjustment of deep learning with original modes and incremental modes. The experimental results of bearing running data demonstrate that the proposed approach could significantly improve the accuracy of diagnosis and save the time cost, contributing to meet the varied needs of the real-time equipment fault diagnosis.https://www.atlantis-press.com/article/25892538/viewDeep learningDynamic compensationFault diagnosisDenoising AutoencoderIncremental learning |
| spellingShingle | Jing Liu Yacheng An Runliang Dou Haipeng Ji Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model International Journal of Computational Intelligence Systems Deep learning Dynamic compensation Fault diagnosis Denoising Autoencoder Incremental learning |
| title | Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
| title_full | Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
| title_fullStr | Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
| title_full_unstemmed | Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
| title_short | Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
| title_sort | dynamic deep learning algorithm based on incremental compensation for fault diagnosis model |
| topic | Deep learning Dynamic compensation Fault diagnosis Denoising Autoencoder Incremental learning |
| url | https://www.atlantis-press.com/article/25892538/view |
| work_keys_str_mv | AT jingliu dynamicdeeplearningalgorithmbasedonincrementalcompensationforfaultdiagnosismodel AT yachengan dynamicdeeplearningalgorithmbasedonincrementalcompensationforfaultdiagnosismodel AT runliangdou dynamicdeeplearningalgorithmbasedonincrementalcompensationforfaultdiagnosismodel AT haipengji dynamicdeeplearningalgorithmbasedonincrementalcompensationforfaultdiagnosismodel |